Deep Gait Tracking With Inertial Measurement Unit
This work addresses precise gait tracking for applications like healthcare or robotics, but it is incremental as it builds on existing IMU and CNN methods.
The paper tackles foot motion tracking using only six-axis IMU sensor data with a convolutional neural network, achieving average errors of 2.30±2.23 cm in X-axis, 0.91±0.95 cm in Y-axis, and 0.58±0.52 cm in Z-axis.
This paper presents a convolutional neural network based foot motion tracking with only six-axis Inertial-Measurement-Unit (IMU) sensor data. The presented approach can adapt to various walking conditions by adopting differential and window based input. The training data are further augmented by sliding and random window samplings on IMU sensor data to increase data diversity for better performance. The proposed approach fuses predictions of three dimensional output into one model. The proposed fused model can achieve average error of 2.30+-2.23 cm in X-axis, 0.91+-0.95 cm in Y-axis and 0.58+-0.52 cm in Z-axis.